




'''simple docstring'''







import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


lowerCAmelCase_     						=				['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
lowerCAmelCase_     						=				{'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
    raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
lowerCAmelCase_     						=				logging.get_logger(__name__)

lowerCAmelCase_     						=				''' Hello world! cécé herlolip'''

lowerCAmelCase_     						=				[
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     [
       'encoder.version',
       'decoder.version',
       'model.encoder.version',
       'model.decoder.version',
       '_float_tensor',
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCAmelCase      ,UpperCAmelCase						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     torch.load(UpperCAmelCase      ,map_location='cpu'						)
   A__       =     torch.hub.load('pytorch/fairseq'      ,'bart.large.cnn'						).eval()
   hub_interface.model.load_state_dict(sd['model']						)
   return hub_interface







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__  ,			A__       =     emb.weight.shape
   A__       =     nn.Linear(UpperCAmelCase      ,UpperCAmelCase      ,bias=UpperCAmelCase						)
   A__       =     emb.weight.data
   return lin_layer







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=None						):






   '''simple docstring'''







   if not os.path.exists(UpperCAmelCase						):
      A__       =     torch.hub.load('pytorch/fairseq'      ,UpperCAmelCase						).eval()
   else:
      A__       =     load_xsum_checkpoint(UpperCAmelCase						)

   bart.model.upgrade_state_dict(bart.model.state_dict()						)
   if hf_checkpoint_name is None:
      A__       =     checkpoint_path.replace('.'      ,'-'						)
   A__       =     BartConfig.from_pretrained(UpperCAmelCase						)
   A__       =     bart.encode(UpperCAmelCase						).unsqueeze(0						)
   A__       =     BartTokenizer.from_pretrained(UpperCAmelCase						).encode(UpperCAmelCase      ,return_tensors='pt'						).unsqueeze(0						)
   if not torch.eq(UpperCAmelCase      ,UpperCAmelCase						).all():
      raise ValueError(
          F"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""						)

   if checkpoint_path == "bart.large.mnli":
      A__       =     bart.state_dict()
      remove_ignore_keys_(UpperCAmelCase						)
      A__       =     state_dict['model.decoder.embed_tokens.weight']
      for src, dest in mnli_rename_keys:
         rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)
      A__       =     BartForSequenceClassification(UpperCAmelCase						).eval()
      model.load_state_dict(UpperCAmelCase						)
      A__       =     bart.predict('mnli'      ,UpperCAmelCase      ,return_logits=UpperCAmelCase						)
      A__       =     model(UpperCAmelCase						)[0]  # logits
   else:  # no classification heads to worry about
      A__       =     bart.model.state_dict()
      remove_ignore_keys_(UpperCAmelCase						)
      A__       =     state_dict['decoder.embed_tokens.weight']
      A__       =     bart.extract_features(UpperCAmelCase						)
      if hf_checkpoint_name == "facebook/bart-large":
         A__       =     BartModel(UpperCAmelCase						).eval()
         model.load_state_dict(UpperCAmelCase						)
         A__       =     model(UpperCAmelCase						).model[0]
      else:
         A__       =     BartForConditionalGeneration(UpperCAmelCase						).eval()  # an existing summarization ckpt
         model.model.load_state_dict(UpperCAmelCase						)
         if hasattr(UpperCAmelCase      ,'lm_head'						):
            A__       =     make_linear_from_emb(model.model.shared						)
         A__       =     model.model(UpperCAmelCase						)[0]

    # Check results
   if fairseq_output.shape != new_model_outputs.shape:
      raise ValueError(
          F"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""						)
   if (fairseq_output != new_model_outputs).any().item():
      raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`'						)
   Path(UpperCAmelCase						).mkdir(exist_ok=UpperCAmelCase						)
   model.save_pretrained(UpperCAmelCase						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
    )
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument(
        '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
    )
    lowerCAmelCase_     						=				parser.parse_args()
    convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)








'''simple docstring'''







def 						_A      ( UpperCAmelCase = 1      ,UpperCAmelCase = 1000						):






   '''simple docstring'''







   A__       =     1
   A__       =     0

   for divide_by_number in range(UpperCAmelCase      ,digit + 1						):
      A__       =     []
      A__       =     numerator
      for _ in range(1      ,digit + 1						):
         if now_divide in has_been_divided:
            if longest_list_length < len(UpperCAmelCase						):
               A__       =     len(UpperCAmelCase						)
               A__       =     divide_by_number
         else:
            has_been_divided.append(UpperCAmelCase						)
            A__       =     now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()


